Strategic pathways to product innovation capabilities in SMEs

Journal of Business Venturing 21 (2006) 75 – 105

Strategic pathways to product innovation capabilities in SMEs Oana Branzei a,*, Ilan Vertinsky b,1 a

b

Schulich School of Business, York University, 4700 Keele Street, Toronto, Ontario M3J 1P3, Canada Sauder School of Business, University of British Columbia, 2053 Main Mall Vancouver BC V6T 1Z2, Canada Received 1 March 2003; received in revised form 1 September 2004; accepted 28 October 2004

Abstract The study articulates a two-dimensional typology of dynamic capabilities, grouping them by the life-cycle stage and the timing of expected returns. Using a cross-industry sample of manufacturing SMEs, we validate and map four distinct innovation strategies onto specific sets of product innovation capabilities. Results show that human capital development efforts catalyze both the external absorption and the internal emergence of novel capabilities. Stronger emphasis on product features and broader market access stimulate the effective replication of extant capabilities, yielding immediate payoffs. Process-focused strategies are a double-edged sword: they facilitate the acquisition and incorporation of external insights yet bound internal capability development. D 2004 Elsevier Inc. All rights reserved. Keywords: SME; Capability; Innovation

1. Executive summary Prior research has documented the positive influence of different types of dynamic capabilities on small firms’ survival prospects and their innovative, financial, and market

* Corresponding author. Tel.: +1 416 736 5096; fax: +1 416 736 5762. E-mail addresses: [email protected] (O. Branzei)8 [email protected] (I. Vertinsky). 1 Tel.: +1 604 822 9406; fax: +1 604 822 6970. 0883-9026/$ - see front matter D 2004 Elsevier Inc. All rights reserved. doi:10.1016/j.jbusvent.2004.10.002

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performance but paid scarce attention to the up-stream association between specific strategic choices and advances or alterations in firms’ sets of dynamic capabilities. This study takes a dynamic capabilities perspective to examine how bundles of innovation strategies shape the ability of SMEs to invent, develop, introduce, and commercialize innovative products. It fleshes out the set of best practices which underpin successful product innovation and tackles two complementary research questions: (a) which strategic maneuvers help nurture different types of dynamic capabilities and (b) to what extent do specific strategic choices motivate distinct capability-development trajectories. Using the dynamic resource-based view as a unifying theoretical framework, we group dynamic capabilities according to their life-cycle stage (Helfat and Peteraf, 2003) and the timing of their expected payoffs (Zahra and George, 2002). We then draw on prior typologies of innovation strategies to identify four distinct strategic triggers and examine their direct and indirect effects on the activation of specific product innovation capabilities by SMEs. Strategic initiatives which guide learning efforts towards capability sets with immediate payoffs can help young or small firms quickly improve their innovative performance, despite their lack of experience and/or their initial resource constraints. Strategic initiatives aimed at rejuvenating stagnating capabilities can help mature SMEs adapt to external trends more swiftly and/or nurture the emergence of internal creative insights more effectively. The hypothesized relationships between innovation strategies and product innovation capabilities were tested on a subpopulation of 3065 SMEs in the Canadian manufacturing sector which introduced at least one new product between 1997 and 1999. The primary collection of firm-level responses regarding their innovation strategies, activities, and outcomes by Statistics Canada was supplemented with a comprehensive set of secondary indicators reflecting firm performance and industry characteristics, some gathered at multiple points in time. Using a combination of exploratory and confirmatory factor analyses on two nonoverlapping, randomly selected halves of the subpopulation of SMEs, we derived and validated four distinct innovation strategies—product, process, market, and human capital development. The direct effects of each innovation strategy on several types of product innovation capabilities were then examined using survey-estimated negative binomial and robust regression models. The findings show that human capital strategies stimulate the emergence of novel capabilities. On one hand, they expand the breadth of external knowledge searches and facilitate the acquisition of innovative ideas from outside sources. On the other, they foster ingenious knowledge cross-pollination within the firm. Strategic emphasis on process efficiency intensifies external learning efforts—complementing the beneficial effects of human capital strategies—but stifles internal creativity. Product development strategies increase SMEs’ knowledge acquisition capabilities and strengthen their efforts to develop and commercialize both new and improved products. Market expansion strategies encourage larger-scale incorporation of novel inputs into existing operations and accelerate the commercialization of radical innovations. Structural equation models provide consistent support for the hypothesized direct effects, confirming the robustness of the findings. They also highlight nested indirect effects of specific strategic initiatives on SMEs’ capability-building efforts, delineating alternate strategic pathways to successful product innovation.

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2. Introduction Innovation captures the gist of entrepreneurial activity (Kirzner, 1979; Schumpeter, 1934). Typically considered bthe lifebloodQ of small start-ups (Acs and Audretsch, 1990), innovation abilities constantly fuel and renew competitive advantage as firms grow and mature (Miller, 1983; Zahra and Covin, 1993). Sustained innovativeness depends on each firm’s set of dynamic capabilities, which helps it bintegrate, build, and reconfigure internal and external competencies to address rapidly changing environmentsQ (Teece et al., 1997: 516) by activating, copying, transferring, synthesizing, reconfiguring, and redeploying different skills and resources (Eisenhardt and Martin, 2000). Dynamic capabilities are broutines through which managers alter their resource base— acquire and shed resources, integrate them together, and recombine themQ (Eisenhardt and Martin, 2000: 1107). They incorporate sets of specific, identifiable processes, or commonly accepted dbest practicesT which can be observed, compared, and sometimes successfully transferred among firms. Idiosyncrasies in dynamic capabilities trigger and sustain interfirm performance differences (Eisenhardt and Martin, 2000; Henderson and Cockburn, 1994). Variance in dynamic capabilities stems in part from firms’ accumulated experience, as prior choices support the development of distinct sets of resources and skills and/or may result in differential effectiveness at generating new value from extant endowments (Schulz, 2003). Recent theoretical developments within the dynamic capability perspective suggest that organizational capabilities evolve over time, and several organizational and environmental levers contribute to their founding, development, maturation, and alteration (Helfat and Peteraf, 2003). Managerial decisions are acknowledged as some of the most critical antecedents of capability transformation (Adner and Helfat, 2003): bunless the external selection environment is so constraining that it limits managers to only one possible option, different managers in different firms may make different choicesQ (Helfat and Peteraf, 2003: 1004). Suitable strategic choices help firms overcome the constraints of their existing resource endowments by guiding the development of extant skills and by facilitating the emergence of new capabilities. These strategic maneuvers can modify firms’ current capability-development trajectories by influencing the range and depth of external cues noticed, the framing of these stimuli as threats or opportunities for action, firms’ speed of reaction, and/or the specific actions taken in response. To our knowledge, this study represents one of the first attempts to examine how several innovation strategies fuel the development of specific product innovation capabilities in SMEs. Innovation strategies, defined as plans that guide firms’ decisions regarding the development and use of technological capabilities (Zahra, 1996: 289), can shape a firm’s technological effectiveness and competitive posture (Park et al., 2002) by specifying the content, sources of desired competencies, and their intended effects (Bierly and Chakrabarti, 1996; Mitchell, 1990). We concentrate on the effects of innovation strategies on the set of dynamic capabilities directly related to firms’ product innovation activities2 (Cohen and 2

Following the definitions of the Oslo Manual (OECD/Eurostat, 1997), product innovation refers to innovative outputs (goods, services, technologies) that have been introduced to the market. It includes products that are new to the firms and significant enhancements or improvements for existing products but excludes minor modifications and/or purely aesthetic changes.

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Levinthal, 1990; Zahra and George, 2002)—i.e., collections of dbest practicesT through which firms gradually access, assimilate, and utilize product-related knowledge generated by outside sources (Deeds et al., 1999: 214) and/or renew and reconfigure their scientific and technological skills (Kogut and Zander, 1992; Peteraf, 1993). This study offers a theoretical framework for classifying firm-level product innovation capabilities and empirically maps the strategic routes that contribute to the development of specific types of capabilities for SMEs. The up-stream connection between strategies and capabilities remains largely understudied. However, recent theoretical studies conceptualize managerial decisions and discretionary strategic choices as essential stepping stones in the capability-building process (Eisenhardt and Martin, 2000; Helfat and Peteraf, 2003; Zahra and George, 2002). Empirical investigations have also uncovered a range of distinct and durable strategic profiles (Bierly and Chakrabarti, 1996; Ostgaard and Birley, 1994; Zahra, 1996) and investigated the requisite collections of ex ante skills which enable or hamper their effective implementation (Chandler and Hanks, 1994; Greene and Brown, 1997). The down-stream association between strategies and performance has attracted sustained research interest and yielded a consistent set of findings. Conditional on environmental and organizational characteristics (Covin et al., 1990; Covin and Slevin, 1990; Kelley and Rice, 2002; Kunkel and Hofer, 1993; McDougall et al., 1992; Sandberg and Hofer, 1987), greater focus on specific strategic choices (Sandberg, 1986) as well as greater stability of these choices over time (Freeser and Willard, 1990) improve firms’ survival chances (McCann, 1991; Duchesneau and Gartner, 1990), stimulate new venture growth, and fuel superior market and financial performance (Keeley and Roure, 1990; Naffziger et al., 1994; Ng et al., 1992; Zahra et al., 1995; Schroeder et al., 2002; Vickery et al., 1993). Several studies have also suggested that the observed association between strategies and performance depends on the boverall abundance of resource-based capabilitiesQ (Chandler and Hanks, 1994: 331) and/or the fit between firms’ strategies and their existing capabilities (Fingenbaum and Karnani, 1991). The question of whether or how firms select and maneuver bundles of strategic interventions to enhance their extant capabilities has remained unaddressed. This study examines which specific types of innovation strategies help trigger, augment, or modify SMEs’ dynamic capabilities. We argue that, as a set, innovation strategies expose and fill significant gaps in firms’ extant skills and resources. Innovation strategies may help firms acquire relevant information from external sources, absorb it into their existing operational routines, transform it into novel product ideas, and deploy it into marketable products (Zahra and George, 2002; George et al., 2002). Our arguments and findings help highlight the direct and indirect effects of innovation strategies on dynamic capabilities, delineating alternate strategic pathways to successful product innovation in SMEs.

3. Theory The theory section introduces a two-dimensional typology which parsimoniously groups the set of dynamic capabilities underpinning firms’ efforts to conceptualize, develop, launch, and commercialize innovative products (Cohen and Levinthal, 1990) according to

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their life-cycle stage (Helfat and Peteraf, 2003) and their payoff schedule (Zahra and George, 2002). We then draw on prior classifications of innovation strategies to examine which specific strategic choices enable the development of each type of capability. 3.1. A typology of dynamic capabilities for product innovation Dynamic capabilities for successful product innovation encompass firms’ abilities to acquire and assimilate external knowledge, transform it into novel, unique competencies and ideas, and then harvest these ideas by first generating and then effectively commercializing new or improved products (Cohen and Levinthal, 1990; Eisenhardt and Martin, 2000; Zahra and George, 2002). The typology presented in Fig. 1 groups product innovation capabilities using two distinct dimensions. The first dimension separates capabilities depending on their life-cycle stage—i.e., whether de-novo capabilities emerge and develop from scratch or whether extant capabilities are cross-pollinated, updated, and adjusted. The second dimension differentiates dynamic capabilities depending on their payoff schedule—i.e., the time lag required before the operational capabilities they engender can translate into firm-level performance improvements. The first dimension was proposed by Helfat and Peteraf (2003), who theoretically distinguish capability emergence and early development from later honing, grafting, and branching. Danneels’ (2002: 1105) grounded investigation of product innovation capabilities similarly differentiates between existing competencies and competencies new to the firm. The initial development of novel capabilities depends on the quantity and quality of human capital—e.g., the knowledge, interaction, task-ability, and willingness of

Fig. 1. A typology of dynamic capabilities for successful product innovation.

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the team members to learn. Hiring, training, improved coordination, and learning by doing facilitate capability emergence (Levinthal and Myatt, 1994). Capability alterations, expansions, and refinements are often motivated by firms’ desire to transfer their extant capabilities to new markets (e.g., replication, Winter and Szulanski, 2001) and/or new product lines (e.g., redeployment, Helfat and Raubitschek, 2000).3 Success hinges on firms’ abilities to delink extant capabilities from the products in which they have been embodied or the markets in which they have been successfully deployed and to relink them to distinct product lines and new market niches (Danneels, 2002). The second dimension was suggested by Zahra and George (2002), who differentiate between bpotentialQ and brealizedQ capabilities. Potential capabilities foster adaptability and constant competence renewal by prompting firms to scan, interpret, and incorporate fresh insights from external sources. These potential capabilities require substantial inflows of new inputs, broad learning, and prolonged sense-making (Lane and Lubatkin, 1998; Mowery et al., 1996). While more intense acquisition and assimilation of external insights can leverage firms’ future capability thresholds, they often clash with firms’ extant knowledge and skills. Thus, before potential capabilities can yield their expected payoffs, firms need to surface and reconcile incongruities between internal and external information streams, overcome absorption barriers, and resolve the significant uncertainty typically associated with dborrowedT knowledge. Realized capabilities, on the other hand, rely and build primarily on each firm’s accumulated internal expertise (Kim, 1997). They help firms more efficiently deepen or branch extant operational routines and/or more quickly recombine prior skills to yield breakthrough insights. When realized capabilities are abundant and fungible, they catalyze a varied range of profitable applications that can quickly translate into superior performance. The proposed capability life-cycle stage and payoff schedule dimensions provide a nuanced distinction among several distinct sets of product innovation capabilities discussed in prior theoretical studies (Cohen and Levinthal, 1990; Zahra and George, 2002). Fig. 1 highlights four groups of dynamic capabilities: acquisition (emergentpotential), assimilation (extant-potential), transformation (emergent-realized), and deployment (extant-realized). Acquisition capabilities can expand firms’ future potential by stimulating the emergence and early development of new knowledge-search routines. Greater efforts to identify, gather, and absorb relevant information from external sources foster new connections, expand firms’ search scope, and increase the speed and quality of learning from external sources (Kim, 1997; Zahra and George, 2002; Mowery et al., 1996). Assimilation capabilities help incorporate novel inputs and ideas into firms’ existing production processes; they refine, update, and extend firms’ prior operational routines (Leonard-Barton, 1995; Rosenkopf and Nerkar, 2001; Schulz, 2003; Szulanski, 1996). Transformation capabilities foster the effective cross-pollination and the conversion of internal expertise into path-breaking insights which stimulate the emergence of de-novo 3

For established capabilities, bfactors external to the capability have a strong enough impact to alter the current development trajectory of the capabilityQ (Helfat and Peteraf, 2003: 1004). These factors may make a capability obsolete, but they often trigger a branching cycle, in which extant capabilities gradually adjust to different conditions.

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operational routines (Christensen et al., 1998; Garud and Nayyar, 1994). Superior transformation capabilities fuel firm-specific operational competencies—which are unique, valuable, and hard to imitate by competitors at least in the short run (Acs and Audretsch, 1990; Cyert and Goodman, 1997; Deeds and Hill, 1996). Thus, they can provide an important, often immediate, source of competitive advantage (Zahra, 1996; Zahra and Covin, 1993). Last, deployment capabilities reflect firms’ abilities to effectively apply extant know-how to new product–market combinations. They require active efforts to distill firms’ extant core competencies, abstracting and disentangling them from existing applications, followed by efforts to quickly adjust these metaroutines to different product–market conditions and implement them effectively across different settings (Danneels, 2002). Fig. 1 also suggests several capability-building routes. Following the capability lifecycle stage axis, emergent capabilities tend to mature, over time, into established capabilities. Acquisition can motivate assimilation. Transformation may accelerate deployment. Following the payoff schedule axis, potential capabilities often provide fresh, different ingredients which stimulate novel recombinations of firms’ realized capabilities. Acquisition of external insights often prompts creative de-novo transformations. Assimilation of new inputs can facilitate redeployment of extant competencies to new product–market combinations.4 Next, we examine to what extent specific strategic maneuvers foster or thwart the development of each of the four types of product innovation capabilities as well as whether some strategies tend to commit firms to specific capability-development routes. 3.2. Connecting innovation strategies with product innovation capabilities The hypotheses map four salient types of innovation strategies—human capital, process, product, and market development5 (Rizzoni, 1991; Tzokas and Saren, 1997)— onto the four mutually exclusive categories of dynamic capabilities discussed above. We propose that human capital development strategies support the emergence of de-novo capabilities—i.e., acquisition and transformation. Product and market development strategies facilitate grafting and branching of extant capabilities—i.e., assimilation and deployment (Helfat and Raubitschek, 2000; Winter and Szulanski, 2001). Active pursuit of 4 At each point in time, previously accumulated skills condition firms’ propensity and ability to develop emergent competencies (Cohen and Levinthal, 1990; Danneels, 2002; Helfat and Peteraf, 2003). As capabilities develop, becoming more mature and finer-grained, they may either promote adaptability and further renewal or they may become core rigidities (Leonard-Barton, 1992), stifling change. 5 Hofer and Sandberg (1987) distinguished five broad types of strategies: product differentiation; quality, service, and price; market or segment domination; innovation; and methods of growth. Miller (1986) identified a comparable set: differentiation, conservative cost control, marketing, product/service innovation, and focus/scope. Empirical studies of entrepreneurial firms highlighted a similar range of strategies: differentiation through quality, marketing differentiation/broad market segmentation/distribution, product innovation, and growth through outside capital (Ostgaard and Birley, 1994). Focusing on technology strategy for SME, Zahra (1996) proposed six finergrained strategic dimensions: pioneer vs. follower, forecasting, the breadth of product innovation, the breadth of process innovation, investment in internal R&D, and reliance on external sources. Similar dimensions were also echoed by Bierly and Chakrabarti (1996) who used four grouping criteria: (a) preference for radical vs. incremental learning; (b) speed of learning; (c) breadth of their knowledge search; and (d) preference for external vs. internal learning.

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process development strategies can leverage potential capabilities by intensifying external knowledge searches and information absorption (Bierly and Chakrabarti, 1996; Ostgaard and Birley, 1994; Zahra and George, 2002). However, when strong path-dependencies hamper creative recombination of internal skills and expertise and/or discourage alternative use of extant skills (Ahuja and Lampert, 2001; Leonard-Barton, 1992), a strong process-focus may backfire, detracting from firms’ abilities to transform and update their current routines. Before developing the rationale for each hypothesized relationship, we briefly review the specific content and implications of each innovation strategy. Development of better human capital consistently enables superior performance (Cooper et al., 1994; Gimeno et al., 1997; Mahoney and Pandian, 1992). Firms which attract highly educated and/or highly skilled workers, provide skill-development and cross-training, and offer continued learning by doing and on the job training for experienced employees develop bdifficult to trade and imitate, scarce, appropriable and specializedQ human capital assets (Amit and Shoemaker, 1993: 36; Becker and Gerhart, 1996; Huselid, 1995). Attracting and retaining high-quality technical and scientific personnel is considered a critical innovation strategy, particularly for high-tech firms (Deeds et al., 1999). Product development strategies capture the intensity of firms’ innovation efforts within a technological domain (Zahra, 1996). They typically support higher levels of innovation and growth (Ardishvili and Cardozo, 1994; McDougall et al., 1994; Shane, 1996). Prior research suggests that young, independent, and technology-based ventures are more likely to seek product breakthroughs than product extensions and modifications (McCann, 1991). Market development strategies reflect the breadth of the geographic markets served and firms’ pursuit of new distribution channels (Ostgaard and Birley, 1994: 289). Extant studies show that high-growth firms are twice as likely as low-growth firms to research and enter new markets (Gundry and Welsch, 1997; Lohmann, 1998). Younger firms are also more likely to expand geographically, using their product lines to serve new regional and international markets, while older firms are more likely to grow locally, developing specialized products for small, established demographic niches (Ardishvili and Cardozo, 1994). Process development strategies typically capture the intensity of innovation efforts aimed at increasing the efficiency and/or the effectiveness of internal production processes (Miller, 1986; Hofer and Sandberg, 1987; Zahra, 1996). Whether viewed as a general competitive weapon or a technology strategy, developing innovative production processes offers a highly contextualized source of rents, which is often very hard to duplicate outside the boundaries of the organization (Leonard-Barton, 1992) and thus provides an effective and sustainable source of differentiation (Ostgaard and Birley, 1994). 3.2.1. Acquisition capabilities Empirical studies have shown that small innovative firms actively scan various external sources of knowledge, seek diverse partnerships (Larson, 1991; Ostgaard and Birley, 1994; Soh, 2003), and can effectively learn from different types of collaborators, including customers, suppliers, universities, and public support agencies (Barringer and Jones, 2000). Broad scanning and timely absorption of new information cues from the external

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environment provide innovative insights (March and Simon, 1958) and help firms develop novel technological competencies (Cohen and Levinthal, 1990; Farrell and Doutriaux, 1994; Teece et al., 1997). Superior acquisition capabilities may prove particularly critical in knowledge intensive, complex, or dynamic environments, where relevant skills tend to be dispersed among highly specialized players (Brown and Eisenhardt, 1995). The direction and intensity of the external learning efforts (Zahra and George, 2002) depend on each firm’s requisite human capital and operational routines. Periodic inflows of qualified personnel and specialized training can foster acquisition capabilities by improving firms’ abilities to effectively span different knowledge bases (Boland et al., 2001; Hitt et al., 2001). Firms that actively seek to improve their operational routines and rejuvenate their production processes may also be more motivated to tap into external sources of information (Ostgaard and Birley, 1994; Vanhaverbeke et al., 2002; Stuart, 2000). H1. Acquisition capabilities will be positively influenced by (a) human capital development strategies and (b) process development strategies. 3.2.2. Assimilation capabilities Gundry and Welsch (1997) have observed that high-growth firms engage in more comprehensive implementation activities than slow-growth firms—e.g., they periodically redesign the layout, update prior operating routines, upgrade their technology, and retrain their production work force. Assimilation capabilities facilitate constant readjustments of preexisting capabilities to accommodate changes in firms’ markets and technologies (Helfat and Peteraf, 2003). Greater emphasis on market development strategies allows firms to better analyze, interpret, and incorporate novel inputs and ideas into existing operations (Zahra and George, 2002) and offers bnew solutions that further enhance the firm’s technological capabilitiesQ (Barkema and Vermeulen, 1998: 8). Active involvement in diverse technological domains also motivates improved incorporation of external technologies (Bierly and Chakrabarti, 1996; Ostgaard and Birley, 1994). Firms willing to frequently update their production processes tend to achieve higher levels of technological proficiency (Helfat, 1994) and operational effectiveness (Spender, 1992; Lane et al., 2001). H2. Assimilation capabilities will be positively influenced by (a) market development strategies and (b) process development strategies. 3.2.3. Transformation capabilities bThe development of new products is an interdisciplinary task that requires the integration of know-how from different areasQ (Deeds and Hill, 1996: 43). Several studies have associated the effective recombination of complementary technological, marketing, and manufacturing know-how with path-breaking technological capabilities (Chakrabarti and Weisenfeld, 1991; Helfat and Peteraf, 2003; Stock et al., 2001). Creative crosspollination of relevant skills and resources across different areas of expertise (Helfat and Peteraf, 2003) depends on the quality and energy of firms’ professional personnel (Deeds et al., 1999). Coombs and Deeds (1996) have shown, for example, that biotech firms employing scientific teams with stronger research records had significantly more

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breakthrough products in the pipeline. Sustained innovativeness also requires firms to constantly nurture novel operational routines by questioning, reshuffling, realigning, and rejuvenating their core skills (Garud and Nayyar, 1994; Mowery and Rosenberg, 1989). A close focus on prior experience often enables incremental improvements but may hamper radical departures from the status-quo through two related mechanisms. First, core competencies often evolve into core rigidities (Leonard-Barton, 1992), which discourage exploration with different approaches (Danneels, 2002). Second, over time firms develop pockets of incompetence that surround their current routines. They tend to endorse familiar, closely related, and/or established project ideas and become less likely to experiment with de-novo competencies (Ahuja and Lampert, 2001). H3. Transformation capabilities will be (a) positively influenced by human capital development strategies and (b) negatively influenced by process development strategies. 3.2.4. Deployment capabilities Effective commercialization of innovative products depends on firms’ abilities to delink extant competencies from established product–market combinations and relink them to new product lines and/or new niches (Danneels, 2002). Prior empirical studies have shown that firms which place stronger emphasis on product differentiation are more likely to pioneer new lines of products (Zahra, 1996) and tend to be more successful at commercializing product innovations (Levie, 1995). Product development strategies ensure first-mover advantages, allow firms to charge premium prices (Covin and Slevin, 1990), and can prevent competitors from tapping into the same niches (Hofer and Sandberg, 1987). The breadth of firms’ target markets can further leverage the benefits associated with the development and commercialization of differentiated product lines (Danneels, 2002; Helfat and Peteraf, 2003; Ostgaard and Birley, 1994), especially if firms can effectively adjust, extend, or replicate their product-development competencies across different niches with little additional effort. H4. Deployment capabilities will be positively influenced by (a) product development strategies and (b) market development strategies.

4. Method 4.1. Data and samples The hypotheses were tested on a representative sample of small- and medium-sized provincial enterprises6 in the Canadian manufacturing sector. Descriptions of innovation activities over a 3-year period, 1997 to 1999, were obtained from The Survey of Innovation 6

The sampling unit for The Survey of Innovation 1999 is the provincial enterprise. For companies with operations within a single industry and with establishments in a single province, the sampling unit is equivalent to the firm. For companies with operations in multiple industries and/or different provinces, all establishments which operate in the same industry and the same province are grouped to form one sampling unit.

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1999, administered by the Science, Innovation, and Electronic Information Division of Statistics Canada. Firm-level performance indicators were extracted for each provincial enterprise from the 1997 Annual Survey of Manufacturers—a census of manufacturing establishments conducted each year by Manufacturing, Construction, and Energy Division of Statistics Canada. Appendix A presents an overview of the average characteristics for the SMEs included in this study to help contextualize our findings. We also compiled a comprehensive set of secondary industry indicators to capture relevant initial discrepancies in firms’ economic, competitive, and knowledge environments and account for differential trends during the study period. These indicators were gathered from multiple data sources, at different points in time, and provide information regarding: domestic markets, export and import patterns (CANSIM), knowledge-intensity (1996 Census), and sources and uses of R&D funds (Annual R&D Survey, the Science, Innovation and Electronic Information Division). Appendix B describes in detail the derivation of the control variables used. They were included in the analyses at the finest level of aggregation available. 4.1.1. Population The Survey of Innovation 1999 targeted Canadian establishments in all manufacturing industries (North American Industry Classification System, NAICS 31–33, Statistics Canada, 1998) and selected natural resource industries (NAICS 1133, 212, 2211). To ensure a match between innovation data and firm-level performance information, the initial sampling frame included lists of respondents to prior production surveys conducted by the System of National Accounts. These surveys provided performance statistics for each respondent—shipments, inventories, labor data, and value-added indicators. All respondents were fully operational in 1997, and new births between 1997 and 1999 were not included in the sampling frame. As a requirement of Statistics Canada to reduce response burden on small businesses, only manufacturing establishments which had at least 20 employees and a gross business income of at least $250,000 in 1997 according to Statistics Canada’s Business Register were considered in the sample selection. While the sampling frame did not include start-ups or very small manufacturing firms, post-hoc analyses using national-level summaries of manufacturing activities from CANSIM showed that respondents to The Survey of Innovation 1999 represented 91.5% of Canadian manufacturing activities in 1997 in terms of manufacturing shipments, 89.7% in terms of manufacturing value-added, and 89.9% in terms of total value added. To obtain a representative cross-section of manufacturing activities across Canada, the design of The Survey of Innovation 1999 was stratified by 31 separate manufacturing industry groupings7 and 12 geographic regions (Canadian provinces and territories). This stratification scheme was applied to respondents to the 1997 Annual Survey of Manufacturers and resulted in 9303 bprovincial enterprisesQ. A subsample of 5944 provincial enterprises was drawn, and a total 5455 completed questionnaires were returned for an overall response rate of 95%. 7

Canadian manufacturing activities cover 21 different 3-digit NAICS codes. The stratification scheme applied to the Survey of Innovation 1999 was slightly finer. NAICS codes 321, 325, 333, 334, and 336 were decomposed into subsets of 4-digit NAICS codes or combinations of 4-digit NAICS codes, resulting in 31 different industry strata (complete descriptions can be obtained from the author).

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To ensure that the estimates represent the national population of manufacturing firms, each bprovincial enterpriseQ received a sampling weight, which represented the inverse of the probability that this respective unit was sampled within its industry*region stratum, given the total number of similar provincial enterprises operational in that stratum in 1997. These weights were adjusted for nonresponse within each stratum and then applied to valid responses to obtain population level estimates. 4.1.2. Studied subpopulation Our analyses were limited to the subpopulation of manufacturing establishments with 100 employees or less (including owners and managers) that have introduced at least one product innovation between 1997 and 1999. Using firm-level performance data extracted from the 1997 Annual Survey of Manufacturers, Appendix A describes the average characteristics of the subpopulation of small- and medium-sized product innovations included in our study, for 21 distinct industries. The average SME included in this study employed 51 workers, reported $9.8 million in total revenues in 1997, and accounted for 0.29% share of the domestic market in its 3-digits NAICS code. 4.1.3. Design and data collection The survey questions were designed by the staff of the Science, Innovation, and Electronic Information Division of Statistics Canada,8 in collaboration with the staff of Industry Canada, Natural Resources Canada, and were pilot-tested with a small sample of firms. Each sampled establishment was precontacted to obtain the name and correct mailing address for a key respondent—in most cases, the owner or CEO or a person directly designated by them. Questionnaires were mailed to each key respondent, with mail, telephone, and fax follow-ups to elicit a response from nonrespondents. Late respondents had the option to provide their answers over the phone by Computer Assisted Telephone Interviewing. We computed the indicators for the criterion and predictor variables by grouping responses to multiple questions. Details on the creation and validation of the measures used are provided next. 4.2. Measures 4.2.1. Criterion variables We examined four types of dynamic capabilities underpinning successful product innovation: acquisition, assimilation, transformation, and deployment (Zahra and George, 2002). Acquisition capabilities were measured as a simple count of the distinct public and private sources from which a firm obtained important ideas for innovative products. Ten fixed categories were provided: trade fairs and exhibitions, Internet or computer-based information networks, professional conferences, meetings and publications, suppliers of equipment, material and components, clients, consultancy firms, universities and colleges, federal or provincial agencies, and research laboratories. Several respondents added to

8

The questionnaire used to gather data for The Survey of Innovation 1999 and national estimates prepared by Statistics Canada are available at: http://www.statcan.ca/english/research/88F0006XIE/88F0006XIB2001010.pdf.

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these categories: individual experts, agent or distributor networks (unsolicited house-calls), and activities sponsored by industry and trade associations. The value of the external idea sourcing indicator ranged from 0 to 14. Assimilation capabilities captured the extent to which firms incorporated different inputs into their existing production processes. Respondents noted whether the most important new or significantly improved product introduced by the firm required the use of new materials, an investment in new machinery or equipment, or the acquisition of new software developed by or specifically for the firm. They also identified whether or not the firm took several active steps directly linked to innovative products and/or processes, including (a) in-house research and development, (b) acquisition of machinery, equipment, or adoption of external technology, (c) industrial engineering and design, (d) retooling or start-up of new production processes, and (e) specific employee training. Their sum provided an indicator of incorporation activities, with a value range from 0 to 8. Two distinct indicators were used for transformation capabilities. The total number of new patent applications filed by each firm from 1997 to 1999 in both Canada and the US indicated the depth of firms’ creative recombination efforts. Patents reflect firms’ accumulated technological expertise (Almeida, 1996; Liebeskind et al., 1996) and their proficiency at crafting inventive products (Bell and McNamara, 1991; Grant, 1996a,b). However, firms’ propensities to patent vary widely across industries. Moreover, within each industry, there are significant discrepancies between the number of pending patents and the number of innovative products launched to the market. Some products are protected by multiple patents, while certain patents are never embodied into tangible products. Our second indicator of transformation capabilities reflected the breadth of SMEs’ innovative efforts by capturing their rates of launching innovative products during a 3-year period, 1997–1999 (Cyert and Goodman, 1997; Deeds and Hill, 1996; Deeds et al., 1999). Responses were recorded in six different categories: 1–2; 3–5; 6–10; 11–20; 21–50; and more than 50 innovative products. To obtain conservative estimates, the analyses used the lower bounds of each response interval. Deployment capabilities reflected a firm’s short-term success at commercializing radical vs. incremental innovations. They were measured as the proportional contribution to 1999 revenues of new and respectively improved products developed during the previous 3 years. Responses were recorded in six different categories: 1–5%; 6–15%; 16– 25%; 26–50%; 51–75%; and 76–100%. For both measures, we used the midpoint of each interval. Sensitivity analyses showed that the reported findings were fully replicated when we used the lower and the upper boundary of each category. 4.2.2. Predictor variables Innovation strategies were operationalized as the average score of multi-item scales. Individual scale items were measured on 5-point Likert scales, with anchors from 1 (low importance) to 5 (high importance). All scale reliabilities exceeded the threshold of 0.60, acceptable for exploratory studies (Nunnally, 1967). We examined the effects of four distinct innovation strategies: human capital, market, product, and process development. Human capital development strategies reflected the strategic importance placed on recruiting knowledgeable employees (experienced employees, skilled people from outside of Canada, new graduates from universities and technical schools), training existing

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employees, and developing functionally diverse teams. The internal reliability of the 6item scale was 0.68. Product development strategies reflected the average emphasis placed on several reasons for undertaking product innovations: (a) to extend product range, (b) to improve product quality, (c) to replace products being phased out, (d) to reduce materials consumption, (e) to reduce environmental damage; and (f) to reduce energy consumption. The internal reliability of the 6-item scale was 0.66. Market development strategies indicated the strategic importance of market creation or expansion. Respondents were asked to rate the importance of three strategic objectives— seeking new markets, developing niche or specialized markets, and developing export markets—for the overall success of their firm. The standardized internal reliability of the 3-item measure was 0.60, consistent with reliabilities of similar measures previously reported for the population of SMEs (Ostgaard and Birley, 1994). Process development strategies reflected the average emphasis placed on several process-related innovation objectives: (a) to reduce labor costs, (b) to increase production capacity, (c) to reduce production time, (d) to improve production flexibility, and (e) to increase speed of delivering products to the market. The internal reliability of the 5-item scale was 0.84. 4.2.3. Predictor validation The scales for the four predictor variables were validated in two-stages. The sample of small- and medium-sized product innovators was randomly split in two. In the first stage, we ran an unconstrained exploratory factor analysis on the first half of the sample. In the second stage, we ran confirmatory factor analyses on the second half of the sample. The exploratory factor analysis provided preliminary evidence for the proposed four types of innovation strategies. Five different factors were extracted, which together explained 55% of the variance. The items used to measure process development, human capital development, and market development strategies, each loaded on a distinct factor. All corresponding items loaded 0.50 or higher on the underlying factor. The market development factor explained 8.2%, human capital development 12.4%, and process development 16.3% of the overall variance. Product development strategies clustered in two separate factors. The former focused on product features, such as miniaturization and safety, and explained 10.7% of the overall variance. The latter reflected product range, quality, and distribution and explained 7.6% of the variance. Confirmatory factor analyses performed on the second half of the sample showed that a measurement model specifying each of the four strategies as a distinct, unidimensional factor fit the data well (NFI=0.984, CFI=0.987, RMSEA=0.062, Chi-square=819.2, df=164, pb0.001) and represented a significant improvement in fit over the one-factor model (Dk 2=1409, Ddf=6, pb0.001). Overall discriminant validity was established by testing a second-order factor model, which spanned all four innovation strategies. This model showed a significant worsening in fit (Dk 2=31.4, Ddf=2, pb0.001) over the fourfactor model. Construct-by-construct discriminant validity was established by comparing the fit of the four-factor model with nested models in which the covariance between each pair of innovation strategies was successively set at 1. All these nested models produced a

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significant worsening in fit (differences in k 2 ranged from 213.6 to 723, for Ddf=1, pb0.001). 4.2.4. Response bias Harman’s one factor test was used to alleviate the concerns that common method biases might artificially inflate the relationships of interest (Podsakoff and Organ, 1986). When all the variables included in the reported analyses were subjected to a factor analysis, 12 factors with Eigenvalues greater than 1 emerged and together explained 86% of the total variance. The first factor accounted for only 16% of this variance, suggesting that common method variance did not have a substantial effect on the findings. 4.3. Analyses 4.3.1. Survey estimators Due to the stratified nature of the survey,9 the relationships between innovation strategies and dynamic capabilities were examined using survey estimation techniques. These techniques employ a robust variance estimator that adjusts the standard errors to take into account the size of each industry*province stratum, the proportion of establishments sampled within each stratum as well as the proportion of valid responses within each stratum. 4.3.2. Negative binomial and robust regression models Since the measures of acquisition, assimilation, and transformation capabilities were based on counts, which took only positive integer values, we used survey-weighted negative binomial models with a robust, pseudo maximum likelihood variance estimator10 to test hypotheses H1, H2 and H3. For this type of criterion variables, OLS regression produces unbiased but inefficient estimates. Survey-weighted regression analyses replicated the reported effects (results are available from the authors). Since the deployment capability measures were based on proportions, we used robust regression models to test H4. As recommended by Draper and Smith (1981), we also verified the robustness of the reported effects using arc-sine and log-odds transformation of the criterion variables (replications using the transformed measures are available from the authors). For both the negative binomial and the robust regression analyses, we ran two sets of models. Model A included only the joint effects of innovation strategies. Model B added

9 Because The Survey of Innovation 1999 was a sample and not a census, all reported relationships between the predictors and criterion variables are representative estimates that accounted for the dual stratification inherent in the sampling design. P P P 10 This was obtained by solving the weighted sample estimating equation: G(b)= h=1L i =1nh j =1mhi w hij S(b; y hij , x hij )=0, where h identifies the strata, i represents the principal sampling unit within each stratum h, and there are j elements within each principal sampling unit. For complex survey data, the weighted blikelihoodQ is not the distribution function of the sample; hence, it is not a true likelihood, and standard likelihood ratio tests are no longer valid. Point estimates remain, however, identical to those from a weighted ordinary maximum likelihood estimator.

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the control variables. The sign, magnitude, and significance of the reported effects were not sensitive to the inclusion of the control variables. We discuss the effects obtained for Model B. 4.3.3. Nested structural equation models Using the weighted covariance matrix, we estimated structural equation models in which (a) acquisition capabilities partially mediated the effect of innovation strategies on assimilation, transformation, and deployment capabilities; (b) assimilation capabilities partially mediated the effect of innovation strategies and acquisition capabilities on transformation and deployment capabilities; (c) transformation capabilities partially mediated the effect of innovation strategies, acquisition, and assimilation capabilities on deployment capabilities. We report the results for the simultaneous test of all three partial mediation effects (full results for the separate models are available from the authors).

5. Results Table 1 presents the correlations between firm-level performance indicators, the predictors, and the criterion variables for the entire sample of small and medium size product innovators in the Canadian manufacturing industry. Size had a positive effect on acquisition and assimilation capabilities. Larger SMEs also launched a larger number of innovative products and were more successful at commercializing new products. Higher levels of differentiation (price–cost margins) stimulated the emergence of de-novo capabilities—expanding SMEs’ searches for external innovative ideas and increasing their propensity to file new patent applications. Higher levels of slack (total margin) encouraged grafting and branching of extant capabilities, showing positive correlations with the number of steps taken to incorporate different inputs into current production processes and with firms’ abilities to commercialize new and improved products. Significant correlations between the predictors and the criterion variables lent preliminary support to all the relationships predicted by H1-H4. The results of the negative binomial model reported in Table 2 support H1. Across different manufacturing industries, human capital and process development strategies stimulated SMEs’ capability to locate and acquire relevant information from outside sources. These findings highlight the critical role of human capital development in building acquisition capabilities, showing that a one-unit increase in the average strategic emphasis placed on developing the quality of their human capital was associated with a 10.2% increase in the average count of external idea sources used. Greater emphasis on efficient processes also intensified SMEs’ external searches for novel solutions (Bierly and Chakrabarti, 1996; Zahra, 1996): a one-unit increase in average process-focus contributed an additional 4.8% increase. H2a and b, which proposed that market and process development strategies would catalyze firms’ assimilation capabilities, were supported. Each unit increase in SMEs’ strategic emphasis on market expansion raised their assimilation capabilities by 7.5%, suggesting that the desire to better serve new markets motivates incorporation of new inputs and corresponding readjustments in manufacturing processes. Each unit increment

Table 1 Zero-order correlations for sampled SMEsa 1

2

3

4

Predictors: innovation strategies 9 Human capital 0.010 development 10 Process development 0.086 11 Product development 0.007 12 Market development 0.033

6

7

8

0.001 0.625 0.367 0.264

0.092 0.213 0.087

0.115 0.233

0.100

9

10

11

12

13

14

15

16

0.021

0.052

0.045

0.100

0.067

0.019

0.091

0.040 0.022 0.044

0.026 0.020 0.067

0.043 0.011 0.031

0.069 0.052 0.024

0.039 0.017 0.006

0.022 0.020 0.058

0.021 0.047 0.050

0.181 0.181 0.240

0.338 0.115

0.160

0.004

0.023

0.037

0.077

0.019

0.158

0.128

0.133

0.041

0.023

0.041

0.019

0.007

0.045

0.111

0.105

0.046

0.070

0.303

0.015

0.035

0.119

0.022

0.077

0.038

0.051

0.005

0.033

0.006

0.024

0.002

0.012

0.023

0.008

0.089

0.016

0.047

0.076

0.031

0.050

0.002

0.004

0.056

0.045

0.017

0.019

0.034

0.041

0.062

0.092

0.090

0.027

0.101

0.003

0.196

0.031

0.062

0.031

0.020

0.025

0.044

0.066

0.081

0.025

0.037

0.011

0.015

0.131

Criterions: dynamic capabilities for product innovation 13 Acquisition: 0.046 0.044 0.055 external idea sourcing 14 Assimilation: 0.103 0.011 0.027 incorporation activities 15 Transformation: 0.000 0.009 0.041 new patent filings 16 Transformation: 0.057 0.022 0.010 product launch rate 17 Deployment: 0.047 0.037 0.039 new product sales 18 Deployment: 0.011 0.007 0.023 improved product sales a

5

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Control variables: firm-level performance indicators 1 Size 2 Domestic 0.252 market share 3 Price–cost margin 0.019 0.053 4 Hourly rate 0.061 0.022 0.335 5 Total margin 0.066 0.096 0.452 6 Production margin 0.090 0.133 0.159 7 Labor intensity 0.061 0.128 0.762 8 Average pay 0.034 0.177 0.038

0.201

Correlations above 0.037 are significant at pb0.05 and are shown in bold.

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Model A: Predictors Human capital Process development Product development Market development F test Significance

H1

H2

H3

H4

Acquisition

Assimilation

Transformation

Deploymentb

External idea sourcing

Incorporation activities

New patent filings

Product launch rate

New product sales

Improved product sales

IRR

IRR

IRR

Sig.

IRR

Coef.

Sig.

Coef.

1.959 0.527 1.256 1.039 F 4,1554=4.12 pb0.01

0.000 0.004 0.114 0.796

0.991 0.896 1.038 0.475 1.125 0.053 1.050 0.355 F 4,1554=2.14 p=0.07

0.001 0.005 0.013 0.015 F 4,1554=6.78 pb0.001

0.840 0.291 0.019 0.003

0.010 0.119 0.007 0.124 0.011 0.052 0.009 0.093 F 4,1554=3.71 pb0.01

Sig.

1.115 0.000 1.048 0.006 1.052 0.006 0.992 0.677 F 4,1554=15.74 pb0.001

Model B: Predictors and control variables Predictors Human capital 1.102 0.000 Process development 1.048 0.008 Product development 1.058 0.003 Market development 0.991 0.652 Control variables Export intensity 1.154 0.032 Exports 1.000 0.856 Imports 1.000 0.144 Fragmentation 0.993 0.394 Entry rate 0.989 0.207 Participant turnover 1.003 0.801

Sig.

1.086 0.003 1.064 0.001 0.994 0.792 1.031 0.172 F 4,1554=7.7 pb0.001

Sig.

Sig.

1.075 1.058 0.999 1.036

0.012 0.005 0.970 0.142

1.623 0.765 1.170 1.055

0.000 0.002 0.200 0.672

1.047 1.017 1.133 1.021

0.453 0.730 0.025 0.681

0.003 0.003 0.013 0.011

0.603 0.544 0.013 0.022

0.009 0.005 0.012 0.011

0.162 0.229 0.021 0.050

0.994 1.000 1.000 1.000 0.984 1.012

0.954 0.152 0.182 0.964 0.119 0.343

3.602 1.000 1.000 1.011 0.936 0.987

0.012 0.005 0.000 0.864 0.175 0.842

1.164 1.000 1.000 1.009 1.027 0.937

0.499 0.002 0.117 0.737 0.272 0.032

0.023 0.000 0.000 0.004 0.007 0.009

0.142 0.934 0.565 0.126 0.002 0.003

0.022 0.000 0.000 0.000 0.001 0.004

0.214 0.070 0.476 0.853 0.524 0.170

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Table 2 Results of negative binomial regression and robust regression modelsa

a

1.006 2.013 1.000 1.001 1.012 1.001 0.997 0.972 1.014 1.011 1.042 1.088 1.009 0.963 0.986 0.983 1.007 0.942 1.000 0.982 1.000 0.732 1.000

0.045 0.529 0.132 0.981 0.049 0.885 0.050 0.793 0.826 0.912 0.671 0.351 0.950 0.744 0.772 0.681 0.804 0.877 0.793 0.963 0.808 0.404 0.764

1.000 1.508 1.000 0.994 1.021 1.012 1.002 1.145 0.989 1.026 1.083 1.135 0.896 0.871 1.027 0.994 0.997 0.553 10.000 2.053 1.000 0.535 1.000

0.938 0.759 0.446 0.880 0.003 0.149 0.320 0.294 0.890 0.828 0.518 0.251 0.531 0.380 0.650 0.901 0.937 0.207 0.959 0.135 0.199 0.197 0.314

1.000 1560 1.000 1.066 0.961 1.026 0.984 1.101 0.579 0.525 0.645 3.171 0.121 1.006 1.891 2.853 0.791 0.350 0.993 2.295 1.003 0.126 1.000

0.998 0.283 0.222 0.785 0.288 0.607 0.087 0.890 0.194 0.306 0.467 0.038 0.012 0.993 0.016 0.001 0.100 0.401 0.300 0.544 0.006 0.172 0.041

0.986 0.043 1.000 1.101 1.013 1.047 1.004 2.336 1.264 1.114 1.832 1.438 1.593 0.564 1.235 1.077 1.078 0.172 1.002 5.951 1.000 0.151 1.000

0.138 0.285 0.781 0.221 0.441 0.016 0.301 0.010 0.203 0.691 0.025 0.181 0.212 0.128 0.122 0.544 0.494 0.011 0.115 0.011 0.047 0.006 0.010

0.001 0.686 0.000 0.021 0.003 0.003 0.000 0.074 0.013 0.022 0.031 0.001 0.076 0.011 0.007 0.004 0.015 0.143 0.000 0.169 0.000 0.146 0.000

0.348 0.024 0.710 0.195 0.049 0.134 0.351 0.011 0.457 0.401 0.219 0.965 0.044 0.713 0.570 0.728 0.033 0.249 0.035 0.174 0.239 0.242 0.529

0.001 0.237 0.000 0.018 0.000 0.000 0.000 0.024 0.029 0.035 0.065 0.026 0.029 0.004 0.015 0.001 0.005 0.035 0.000 0.077 0.000 0.038 0.000

0.244 0.485 0.359 0.373 0.966 0.930 0.230 0.441 0.187 0.223 0.033 0.299 0.503 0.911 0.214 0.915 0.564 0.775 0.576 0.536 0.621 0.752 0.727

The results for the negative binominal regression models are reported as incidence-rate ratios (IRR). The results for the robust regression models are reported as unstandardized regression coefficients (Coef.). The significance levels for both the incidence-rate ratios and the unstandardized regression coefficients (sig.) are based on two-tailed tests. Effects of the predictors on the criterion variables significant at pb0.05 appear in bold. The results are based on 1652 unweighted responses, which represent a subpopulation of 3065 SMEs in the Canadian manufacturing sector. b Arc-sine and log odds transformations of the criterion variables measuring deployment capabilities did not change the magnitude or significance of the robust regressions results reported above (Draper and Smith, 1981).

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Knowledge intensity R&D intensity DR&D expenditures DR&D personnel Foreign R&D funding Foreign R&D performers Alternate R&D sources Human capital scarcity Technology obsolescence Product obsolescence Input substitution Output substitution Information asymmetry Dynamism Appropriability regime Size Domestic market share Price–cost margin Hourly rate Total margin Production margin Labor intensity Average pay

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in the emphasis placed on process efficiency contributed an additional 5.8% increase in assimilation capabilities. Our results also suggest that while replication of existing competencies in new markets and recombination and refinement of extant processes help boost firms’ assimilation capabilities, redeploying competencies to new product lines leaves current operational routines unchanged. These insights corroborate the theoretical arguments of Danneels (2002) and Helfat and Peteraf (2003) and extend them to a large representative sample of small- and medium-sized product innovators. H3 proposed that SMEs would achieve stronger transformation capabilities when they attract and maintain superior human capital and when they place lower emphasis on process development strategies. The results support the hypothesized effects of human capital and process development strategies on creative transformation—which was operationalized as the number of patent filings. Higher quality of firms’ professional staff fueled creative juices and motivated active pursuit of technical and scientific discoveries or unique and useful combinations of extant knowledge. Greater emphasis on efficient production routines seemed to bound firms to their current technological abilities, triggering core rigidities. Familiarity, propinquity, and maturity may expose established firms to treacherous learning traps, shifting attention to the known, favoring closely related developments over path-breaking ones, and discouraging experimentation with novel ideas (Ahuja and Lampert, 2001). Intriguingly, however, neither the quality of the personnel nor a greater focus on process efficiency influenced firms’ sustained innovativeness, operationalized as the number of product launches over a 3-year period. Only efforts to differentiate product features ensured a sustained pace of innovative product launches (13.3%). These results lend partial support to H3a and b and highlight an interesting bifurcation in SMEs’ transformation capabilities. The first subset of transformation capabilities, which fosters creative insights, benefits from human capital and is thwarted by excessive attention to extant processes. The second subset of transformation capabilities, which facilitates the sustained embodiment of resources and skills into innovative products, depends only on firms’ product differentiation efforts. Notably, we found no association between these two subsets of transformation capabilities over the 3-year period surveyed in this study (Table 1). Last, H4 proposed that greater emphasis on product and market development strategies improves SMEs’ deployment capabilities. The results lent strong support to H4a, showing that greater emphasis on product features supports improved commercialization for both new and improved products. H4b, however, received mixed support. Market expansion strategies accelerated the commercialization of new products. SMEs that developed and commercialized break-through innovations were more likely to seek and enter new geographic markets. Zahra (1996) also found that SMEs with strong motives to expand their operations internationally tended to pursue radical innovations more aggressively, seeking a first-mover advantage through pioneering strategies. Contrary to the general association between geographic expansion strategies and incremental exploitation of firms’ current product bases (Oviatt and McDougall, 1994), we found that more intense market development strategies constrained exploitation while an increased focus on existing niches fostered the commercialization of incremental innovations.

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5.1. Strategic pathways to product innovation capabilities The structural equation model shown in Fig. 2 corroborates the direct relationships discussed in H1-H4 and suggests that innovation strategies also affect SMEs’ dynamic capabilities indirectly. Their influence on realized capabilities compounds, or attenuates, over time, contingent on the development of potential capabilities (Zahra and George, 2002). The model presented in Fig. 2 suggests that human capital development strategies have a direct positive effect on SMEs’ acquisition, assimilation, and creative transformation capabilities. Their influence on sustained transformation is mediated by acquisition capabilities. Similarly, process development strategies do not affect deployment capabilities directly. However, they fuel acquisition and assimilation capabilities, which support a higher launch rate and more effective product commercialization.

Fig. 2. Pathways to product innovation capabilities in SMEs—structural equation model. Note: The structural equation model used multiple indicators for each predictor (human capital, process, product, and market development innovation strategies). Each latent construct representing the criterion variables was measured with a single item indicator. The number of innovative ideas sourced externally specified acquisition capabilities. The number of incorporation activities underpinned assimilation capabilities. Two separate latent constructs were designed to distinguish two distinct types of transformation capabilities: the depth of SMEs’ creative efforts (the number of new patent filings) and their sustained breadth (product launch rate). Two additional latent constructs captured firms’ deployment capabilities: radical innovation capabilities (new product sales) and incremental innovation capabilities (improved product sales). All error-term variances were set at zero to provide the most conservative set for the model. In addition to the direct effects of innovation strategies on product innovation dynamic capabilities, the structural equation model examined the indirect strategic pathways to product innovation capabilities by specifying a nested sequence of partial mediation effects—by acquisition capabilities (on assimilation, transformation, and deployment latent constructs), assimilation capabilities (on transformation and deployment latent constructs), and transformation capabilities (on deployment latent constructs). The joint mediation model shown above, which included all the direct and indirect paths linking strategies and dynamic capabilities, fits the data well (NFI=0.983, CFI=0.985, RMSEA=0.055, Chi-square=2706.29, df=262, pb0.001). Neither the magnitude nor the significance of the reported effects changed when each mediation effect was assessed independently, validating the robustness of the reported pathways. The figure shows only the paths with coefficients significant at pb0.05 (one-tailed tests). The direct effects appear as straight continuous lines. The indirect effects are represented as curved dotted lines (their patterns vary to facilitate visual interpretation).

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The SEM findings also corroborate the surprising isolation of creative transformation capabilities noted under H3. They neither benefited from superior acquisition or assimilation capabilities nor supported superior deployment capabilities, at least in the short run. Prior findings suggest that this disconnect persists for even longer time lapses between invention and launch (Deeds et al., 1999) and could be in part attributed to industry variance in firms’ propensity to patent. Funding and reward structures encourage patenting in certain industries (e.g., biotech, high-tech computing). In mature industries, however, patents can expose valuable insights to competitors, and firms may prefer secrecy. The observed short-term disconnect between creative transformation capabilities on one hand and sustained transformation and deployment capabilities on the other could also indicate a temporary focus on either exploration or exploitation. The results suggest that SMEs navigate a revolving cycle of multiple years of intense creation followed by several years of effective application rather than constantly seeking to balance their exploration and exploitation efforts (March, 1991). The duration of this creation– application cycle could vary significantly across manufacturing industries. Long cycles have become accepted industry norms in the biotech industry. With the exception of biotech, however, very little is currently known about the timing, duration, and triggers of exploration–exploitation cycles. Understanding how SMEs balance patentable ideas with product offerings over time can shed light on managerial imperatives specific to each stage and on transitions from one stage to the next. During long creative (exploration) rounds, SMEs may face difficulties sustaining their R&D expenses. During long application (exploitation) rounds, SMEs may struggle to attract, retain, or motivate valued researchers. Tracking shifts in different types of dynamic capabilities during stage transitions offers an extremely worthwhile research avenue. 5.2. Limitations These findings are subject to several limitations. First, we advise caution in generalizing the findings to manufacturing SMEs that operate in different geo-political and economic environments or nonmanufacturing SMEs in Canada and elsewhere. Our survey-weighted estimates are specific to the subpopulation of Canadian manufacturing SMEs with less than 100 employees, which have adopted at least one product innovation between 1997 and 1999. For this subpopulation, the double stratification scheme used by The Survey of Innovation 1999 ensures good representation of Canadian manufacturing SMEs both across different industries and across different geographical locations. Second, our findings are time-delimited. The 1997–1999 had several unique characteristics that could have motivated higher than average levels of innovative activities among respondent firms. Annual domestic market grew at an average rate of 8.47% during this period.11 We controlled for temporary surges in industry demand and/or resulting fluctuations in SMEs’

11

While average annual growth rates varied widely among different manufacturing industries (from a minimum of 12.5% in the Leather, tanning, and finishing industry to +47.6 in Nonferrous metal production and processing industry), for the majority of the industries surveyed 1997–1999 was a period of prosperous growth, with average annual growth rates of domestic markets oscillating around 10%.

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innovative activities by including a broad and versatile range of controls at the firm and industry level (Appendix B). However, the robustness of the reported results over time can only be completely validated by replicating the findings for the same subpopulation for different time periods. Third, causal interpretations of the relationships between innovation strategies and dynamic capabilities are advanced with caution since the variables measuring strategies and capabilities were collected through the same instrument (The Survey of Innovation 1999). Prior empirical studies suggested that small firms’ innovation strategies show remarkable stability over time (Bierly and Chakrabarti, 1996). We measure innovation strategies using reliable composite indicators that combine orientation and action items, which help capture the bpattern of set choices taken at a point in time and over timeQ (McCann, 1991: 189; Ostgaard and Birley, 1994). Dynamic capabilities, in contrast, are considered more fleeting and rapidly evolving (Eisenhardt and Martin, 2000). Operating under the assumption that the passing of time increases the chances that a firm would learn from more external players, modify its production processes, develop patentable inventions, and introduce additional products, we opted for holistic cumulative measures for potential capabilities (acquisition and assimilation) and for emergent capabilities (creative and sustained transformation). Deployment capabilities were carefully delimited in time (commercialization activities in 1999) and differentiated in scope (new vs. improved products invented during the time frame of the study). Appendix C illustrates how the levels of the examined product innovation capabilities vary across manufacturing industries. Our confidence in the directionality of the reported findings is strengthened by prior longitudinal findings that partially corroborate some of our findings (Deeds et al., 1999; George et al., 2002) and by the robustness of the direct effects of strategies on dynamic capabilities in structural equation models which delineate complex capability-building pathways. Last, the data were provided by single informants. Reliance on CEOs and knowledgeable senior executives is consistent with prior evidence that they possess the most complete and up-to-date information about entrepreneurial firms’ technological and innovation decisions and their outcomes (McCann, 1991; Zahra and Covin, 1993). Future studies may, however, attempt to gather information from multiple respondents to verify to what extent CEOs’ perceptions of firm-level innovation strategies and capabilities are echoed by other employees. 5.3. Future research The findings provide a glimpse at the set of innovation strategies which help SMEs nurture select capabilities and untangle the web of pathways that directly and indirectly boost their observable product innovation capabilities. Future studies may extend the current set of findings in several important directions. First, researchers can explore whether, and how, different types of external relationships may help SMEs initiate or sustain different capability development pathways (George et al., 2002; Soh, 2003). Since professional expertise, managerial attention, and funds are often scarce for SMEs, reliance on internal efforts may prematurely curtail the development of relevant

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capabilities. Evidence that small initial investments can be leveraged over time may encourage SMEs to persist in their internal efforts. Strengthening the links between different types of resources, strategies, capability-development paths, and their performance consequences can also boost the confidence of external parties, including the financial community, that expected returns are within reach and thus motivate them to lend additional support during the development of novel or potential capabilities. Second, future studies need to explore in greater detail the positive or negative consequences of strategic focus (i.e., pursuing a single path full-speed) vs. breadth (i.e., following multiple paths simultaneously). Third, future studies can examine to what extent specific industry conditions force an optimal path of technological development or leave room for managerial decision. Fourth and last, it is important to understand which factors trigger shifts in firms’ prior strategic mixes and the consequences of their attempted strategic redirections.

6. Conclusion This study examines how SMEs’ strategic choices impact their ability to conceptualize, develop, introduce, and commercialize new products. It introduces a parsimonious classification of SMEs’ dynamic capabilities and empirically tests how different innovation strategies guide SMEs through the nested sequence of dynamic capabilities which underpin their product innovation efforts. The findings bring a twofold contribution to the literature. First, we articulate and test an integrative framework that helps delineate and assess specific types of product innovation capabilities. Second, we link specific innovation strategies with alternate routes for building acquisition, assimilation, transformation, and deployment capabilities. Taken together, the study identifies which innovation strategies are most beneficial for SMEs at different stages of capability building (Helfat and Peteraf, 2003) and suggests that alternate strategic pathways foster exploration vs. exploitation cycles (March, 1991).

Acknowledgements This paper benefited greatly from discussions with Gerard George, Masao Nakamura, Martin Schulz, Mark-David Seidel, Stewart Thornhill, Harry Sapienza, Saras Sarasvathy, Shaker Zahra, and participants at the First West Coast Research Symposium on Technology Entrepreneurship sponsored by the University of Washington. The Science, Innovation, and Electronic Information Division of Statistics Canada facilitated access to the data. We thank Frances Anderson, Julio Rosa, Guy Sabourin, and Susan Schaan for sharing their experience and insights. The views expressed herein are those of the authors and do not necessarily reflect the opinions of Statistics Canada. SSHRC provided financial support through SSHRC grant number 412-98-0025, MRCI: Entrepreneurship Research Alliance. Research fellowships from SSHRC and the University of British Columbia are also gratefully acknowledged by the first author.

Appendix A. Survey-weighted mean estimates of firm-level performance indicators for sampled SMEs Firm size

Market share (%)

Total ships.

TVA

Total margin (%)

Mfg. ships.

MVA

Production margin

Price–cost margin (%)

Labor intensity (%)

311 Food manufacturing 313 Textile mills 314 Textile product mills 315 Clothing manufacturing 316 Leather and allied product manufacturing 321 Wood product manufacturing 322 Paper manufacturing 323 Printing and related support activities 324 Petroleum and coal products manufacturing 325 Chemical manufacturing 326 Plastics and rubber products manufacturing 327 Nonmetallic mineral product manufacturing 331 Primary metal manufacturing 332 Fabricated metal product manufacturing 333 Machinery manufacturing 334 Computer and electronic product manufacturing 335 Electrical equipment, appliance and component manufacturing 336 Transportation equipment manufacturing 337 Furniture manufacturing 339 Misc. manufacturing

47 61 48 53 53

0.25 1.13 0.51 0.37 3.02

14.787 9.199 5.397 6.082 5.866

4.315 4.596 2.059 2.770 2.222

33.77 48.55 39.30 47.22 51.99

13.83 8.90 4.80 5.78 5.86

4.173 4.629 1.987 2.713 2.239

138.57 94.15 58.70 89.15 51.98

14.35 20.93 14.45 21.99 22.96

19.18 25.86 24.17 25.11 28.93

50 53 51

0.15 0.16 0.07

8.722 13.316 6.650

3.148 4.685 3.666

39.83 39.08 55.09

8.43 12.92 6.52

3.079 4.715 3.667

83.28 122.53 103.94

18.27 20.27 24.29

20.89 18.82 30.65

35

0.11

24.711

5.668

36.11

22.56

5.129

617.58

27.16

8.71

46 54

0.46 0.09

21.829 10.826

7.859 4.799

39.73 47.46

18.16 9.65

7.210 4.568

335.51 121.38

23.83 27.40

15.57 19.81

41

0.39

7.710

3.749

49.62

6.63

3.428

117.09

23.73

25.71

59 54

0.34 0.37

16.982 8.837

6.332 4.068

46.78 52.00

15.95 8.18

6.270 3.950

139.81 102.88

26.27 23.62

19.64 28.07

54 52

0.30 0.33

9.431 10.417

5.028 5.182

55.04 49.00

8.68 9.48

4.818 4.596

139.33 169.43

26.02 24.29

28.83 26.41

52

0.35

9.138

4.185

48.83

8.52

4.021

109.98

8.14

40.72

53

0.25

7.781

3.767

50.41

7.21

3.666

98.83

22.58

26.95

56 46

0.35 0.22

6.853 6.368

3.400 3.338

51.42 56.37

6.66 5.68

3.392 3.107

82.67 109.55

25.95 26.91

25.70 29.04 99

Source: 1997 Annual Survey of Manufacturers, as compiled by the authors. Values for total shipments, manufacturing shipments, total value added (TVA), and manufacturing value added (MVA) are shown in million CAN$.

O. Branzei, I. Vertinsky / Journal of Business Venturing 21 (2006) 75–105

Industry (3-digit NAICS code)

Level and measure

Export intensity Total exports Total imports Fragmentation Rate of entry Participant turnover Knowledge workers R&D intensity DR&D expenditures DR&D personnel Foreign R&D funding Foreign R&D performers Alternate R&D sources Knowledge scarcity

CANSIM 1997 CANSIM 1997 CANSIM 1997 ED 1999 ED 1999 ED 1998–1999 Census 1996 SIEID 1997 SIEID 1997–1999 SIEID 1997–1999 SIEID 1997–1999 SIEID 2000 SIEID 2000 SI 1999

Technology obsolescence Product obsolescence Input substitution Output substitution Information asymmetry

SI SI SI SI SI

Dynamism

SI 1999

Appropriability regime Size Domestic market share Price–cost margin Hourly rate Total margin Production margin Labor intensity Average pay

SI 1999 ASM 1997 ASM 1997 ASM 1997 ASM 1997 ASM 1997 ASM 1997 ASM 1997 ASM 1997

4-digit NAICS, total exports/size of the domestic market 4-digit NAICS, total exports 4-digit NAICS, total imports 3-digit NAICS, proportion of SMEs (firms under 100 employees) in the industry 3-digit NAICS, proportion of new entrants to the industry in 1999 3 digit NAICS, net change in the total number of industry participants from 1998 to 1999 3-digit NAICS, proportion of knowledge workers in the industry 4-digit NAICS, intramural R&D investments divided by total revenues 4-digit NAICS, average change in intramural R&D expenditures 4-digit NAICS, average change in the number of PhDs and masters employed by R&D departments 4-digit NAICS, annual average of foreign-funded R&D, by firm size category 4-digit NAICS, proportion of foreign-controlled R&D performers 4-digit NAICS, proportion of R&D funds controlled by non-Canadian or noncommercial sources 4-digit NAICS, sum of two items: bIt is difficult to hire qualified staff and workersQ and bIt is difficult to retain qualified staff and workersQ 4-digit NAICS, bProduction technologies change rapidlyQ 4-digit NAICS, bMy products quickly become obsoleteQ 4-digit NAICS, bMy firm can easily replace its current suppliersQ 4-digit NAICS, bMy clients can easily substitute my products for the products of my competitorsQ 4-digit NAICS, average of two items (both reverse coded): bMy competitors’ actions are easy to predictQ and bMy client’s demands are easy to predictQ 4-digit NAICS, average of two items: bThe arrival of new competitors is a constant threatQ and bThe arrival of competing products is a constant threatQ 4-digit NAICS, average number of different intellectual property protection means used by firms Natural logarithm of the total employment, including working owners and partners in 1997 Total shipments/size of the domestic market (CANSIM 1997) Total shipments less total costs (material, fuel, and personnel ) divided by total shipments Human capital expenditures (salaries and wages) divided by the total number of paid hours Total value added divided by total production Manufacturing value added divided by the total number of production employees Human capital expenditures (salaries and wages) divided by total shipments Human capital expenditures (salaries and wages) divided by the number of paid employees

a

1999 1999 1999 1999 1999

These control measures were computed by the authors. Unless otherwise indicated, measures are computed at the firm-level, and responses are reported as the mean score of a single item on a 5-point Likert-type scale. Secondary sources of data include ASM 1997—Annual Survey of Manufacturers; the CANSIM database; ED, 1983–2000—Statistics Canada’s Employment Dynamics; SI 1999—The Survey of Innovation; SIEID—Science Innovation and Electronic Information Division of Statistics Canada, bIndustrial Research and Development 2002 Intentions (with 2001 preliminary estimates and 2000 actual expenditures)Q, 88-202-XIB.

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Sourcesa

100

Appendix B. Sources, levels, and measures for industry and firm-level control variables Control variables

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101

Appendix C. Survey-weighted mean estimates of criterion variables for sampled SMEs Industry (3-digit NAICS code)

Dynamic capabilities for product innovation Acquisition Assimilation

311 Food manufacturing 313 Textile mills 314 Textile product mills 315 Clothing manufacturing 316 Leather and allied product manufacturing 321 Wood product manufacturing 322 Paper manufacturing 323 Printing and related support activities 324 Petroleum and coal products manufacturing 325 Chemical manufacturing 326 Plastics and rubber products manufacturing 327 Nonmetallic mineral product manufacturing 331 Primary metal manufacturing 332 Fabricated metal product manufacturing 333 Machinery manufacturing 334 Computer and electronic product manufacturing 335 Electrical equipment, appliance and component manufacturing 336 Transportation equipment manufacturing 337 Furniture manufacturing 339 Miscellaneous manufacturing

Transformation Deployment

External idea sourcing

Incorporation New activities patent filings

Product New Improved launch product product rate sales (%) sales (%)

3.64 5.28 2.92 3.38 4.31

4.17 6.25 3.71 3.26 4.25

0.34 0.56 0.88 0.10 0.00

9.53 9.75 9.16 17.27 32.25

12.43 14.15 19.43 19.68 12.80

10.53 19.46 13.75 19.89 15.15

3.35 4.03 3.92

4.52 4.64 4.27

0.24 1.32 0.17

4.51 9.65 7.66

14.25 8.52 12.96

12.45 14.20 16.01

4.36

5.82

0.00

5.73

7.05

13.18

3.67 4.05

4.46 4.95

1.76 5.62

9.36 6.50

11.10 11.73

10.83 12.03

4.16

3.89

0.53

4.93

11.68

13.15

4.24 3.89

4.77 4.56

0.38 1.13

8.73 6.35

13.02 10.50

12.24 11.31

3.92 4.29

4.17 4.65

1.29 1.02

6.46 5.21

13.62 15.21

15.58 18.86

4.11

4.32

4.19

9.45

15.58

15.98

3.30

4.29

2.01

6.95

16.36

15.76

3.34 3.76

3.73 4.27

0.46 0.85

8.64 7.90

15.10 16.68

13.39 14.93

Source: The Survey of Innovation 1999, as compiled by the authors.

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